Learning automata: an introduction
Learning automata: an introduction
New Topics in Learning Automata Theory and Applications
New Topics in Learning Automata Theory and Applications
Intelligent navigation of autonomous vehicles in an automated highway system: learning methods and interacting vehicles approach
Automatic control based on wasp behavioral model and stochastic learning automata
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Multiple stochastic learning automata for vehicle path control in an automated highway system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Generic reinforcement schemes and their optimization
ECC'11 Proceedings of the 5th European conference on European computing conference
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Reinforcement schemes represent the basis of the learning process for stochastic learning automata, generating their learning behavior. An automaton using a reinforcement scheme can decide the best action, based on past actions and environment responses. The aim of this paper is to introduce a new reinforcement scheme for stochastic learning automata. We test our schema and compare with other nonlinear reinforcement schemes. The results reveal a faster convergence of the new schema to the "optimal" action.